Overview

Dataset statistics

Number of variables46
Number of observations74599
Missing cells52043
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 MiB
Average record size in memory376.0 B

Variable types

Categorical26
Numeric18
Text2

Alerts

ciudad has constant value ""Constant
operacion has constant value ""Constant
tipologia_imueble has constant value ""Constant
amueblado is highly imbalanced (75.6%)Imbalance
arico is highly imbalanced (84.2%)Imbalance
duplex is highly imbalanced (82.5%)Imbalance
estudio is highly imbalanced (81.8%)Imbalance
nueva_construccion is highly imbalanced (80.9%)Imbalance
orientacion_n is highly imbalanced (50.7%)Imbalance
ano_construccion has 44040 (59.0%) missing valuesMissing
exterior_interior has 4996 (6.7%) missing valuesMissing
n_piso has 3006 (4.0%) missing valuesMissing
ano_construccion is highly skewed (γ1 = -22.94340661)Skewed
precio_parking is highly skewed (γ1 = 54.35967012)Skewed
n_habitaciones has 2155 (2.9%) zerosZeros
n_piso has 7852 (10.5%) zerosZeros

Reproduction

Analysis started2024-02-28 08:50:51.718066
Analysis finished2024-02-28 08:51:26.731493
Duration35.01 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

a_reformar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
60617 
1
13982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%

Length

2024-02-28T08:51:26.788293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:26.870383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60617
81.3%
1 13982
 
18.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
41086 
1
33513 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

Length

2024-02-28T08:51:26.953825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:27.036982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

Most occurring characters

ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41086
55.1%
1 33513
44.9%

amueblado
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
3
69902 
2
 
3684
1
 
1013

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

Length

2024-02-28T08:51:27.120094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:27.189453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 69902
93.7%
2 3684
 
4.9%
1 1013
 
1.4%

ano_construccion
Real number (ℝ)

MISSING  SKEWED 

Distinct181
Distinct (%)0.6%
Missing44040
Missing (%)59.0%
Infinite0
Infinite (%)0.0%
Mean1964.5289
Minimum1
Maximum2291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:27.288194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1900
Q11955
median1968
Q31986
95-th percentile2008
Maximum2291
Range2290
Interquartile range (IQR)31

Descriptive statistics

Standard deviation56.535057
Coefficient of variation (CV)0.028777921
Kurtosis752.44229
Mean1964.5289
Median Absolute Deviation (MAD)16
Skewness-22.943407
Sum60034039
Variance3196.2127
MonotonicityNot monotonic
2024-02-28T08:51:27.403479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 1796
 
2.4%
1970 1663
 
2.2%
1965 1448
 
1.9%
1900 1309
 
1.8%
1950 709
 
1.0%
1930 631
 
0.8%
1940 625
 
0.8%
1966 604
 
0.8%
2006 581
 
0.8%
1969 580
 
0.8%
Other values (171) 20613
27.6%
(Missing) 44040
59.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
19 3
< 0.1%
48 1
 
< 0.1%
49 2
< 0.1%
50 2
< 0.1%
54 1
 
< 0.1%
160 1
 
< 0.1%
173 1
 
< 0.1%
ValueCountFrequency (%)
2291 1
 
< 0.1%
2020 10
 
< 0.1%
2019 80
 
0.1%
2018 300
0.4%
2017 152
0.2%
2016 39
 
0.1%
2015 40
 
0.1%
2014 32
 
< 0.1%
2013 49
 
0.1%
2012 43
 
0.1%

area_construida
Real number (ℝ)

Distinct541
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.22397
Minimum21
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:27.503492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile40
Q162
median83
Q3117
95-th percentile225
Maximum985
Range964
Interquartile range (IQR)55

Descriptive statistics

Standard deviation66.907195
Coefficient of variation (CV)0.66098173
Kurtosis17.491326
Mean101.22397
Median Absolute Deviation (MAD)25
Skewness3.1765868
Sum7551207
Variance4476.5728
MonotonicityNot monotonic
2024-02-28T08:51:27.603371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2460
 
3.3%
70 2300
 
3.1%
80 2124
 
2.8%
65 1905
 
2.6%
75 1854
 
2.5%
90 1712
 
2.3%
50 1580
 
2.1%
100 1521
 
2.0%
55 1377
 
1.8%
110 1252
 
1.7%
Other values (531) 56514
75.8%
ValueCountFrequency (%)
21 47
 
0.1%
22 50
 
0.1%
23 42
 
0.1%
24 55
 
0.1%
25 191
 
0.3%
26 54
 
0.1%
27 100
 
0.1%
28 112
 
0.2%
29 61
 
0.1%
30 508
0.7%
ValueCountFrequency (%)
985 1
 
< 0.1%
982 1
 
< 0.1%
941 1
 
< 0.1%
934 1
 
< 0.1%
928 1
 
< 0.1%
926 1
 
< 0.1%
900 2
< 0.1%
894 4
< 0.1%
850 1
 
< 0.1%
847 1
 
< 0.1%

arico
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72882 
1
 
1717

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

Length

2024-02-28T08:51:27.703342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:27.787720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72882
97.7%
1 1717
 
2.3%

armarios
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
42699 
0
31900 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

Length

2024-02-28T08:51:27.919914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:28.019903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

Most occurring characters

ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 42699
57.2%
0 31900
42.8%

ascensor
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
51859 
0
22740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

Length

2024-02-28T08:51:28.102949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:28.186354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

Most occurring characters

ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 51859
69.5%
0 22740
30.5%

barrio
Text

Distinct135
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:28.404470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length28
Mean length12.074331
Min length3

Characters and Unicode

Total characters900733
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCuatro Vientos
2nd rowCasa de Campo
3rd rowFuente del Berro
4th rowCasa de Campo
5th rowHuertas-Cortes
ValueCountFrequency (%)
de 6709
 
5.7%
campo 3766
 
3.2%
casa 3340
 
2.8%
3159
 
2.7%
ciudad 3023
 
2.6%
san 2623
 
2.2%
universitaria 2562
 
2.2%
lavapiés-embajadores 2149
 
1.8%
rosas 1957
 
1.7%
malasaña-universidad 1941
 
1.6%
Other values (166) 86945
73.6%
2024-02-28T08:51:28.702899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 134891
15.0%
e 75892
 
8.4%
s 67555
 
7.5%
r 58639
 
6.5%
i 53387
 
5.9%
o 49815
 
5.5%
l 46184
 
5.1%
43575
 
4.8%
n 35360
 
3.9%
d 34945
 
3.9%
Other values (47) 300490
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 727834
80.8%
Uppercase Letter 115137
 
12.8%
Space Separator 43575
 
4.8%
Dash Punctuation 13179
 
1.5%
Decimal Number 1008
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 134891
18.5%
e 75892
10.4%
s 67555
9.3%
r 58639
 
8.1%
i 53387
 
7.3%
o 49815
 
6.8%
l 46184
 
6.3%
n 35360
 
4.9%
d 34945
 
4.8%
t 24230
 
3.3%
Other values (20) 146936
20.2%
Uppercase Letter
ValueCountFrequency (%)
C 21588
18.7%
P 9961
 
8.7%
V 9326
 
8.1%
A 9052
 
7.9%
E 8246
 
7.2%
L 7604
 
6.6%
M 6501
 
5.6%
B 4601
 
4.0%
U 4503
 
3.9%
S 4470
 
3.9%
Other values (13) 29285
25.4%
Decimal Number
ValueCountFrequency (%)
1 504
50.0%
2 504
50.0%
Space Separator
ValueCountFrequency (%)
43575
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 842971
93.6%
Common 57762
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 134891
16.0%
e 75892
 
9.0%
s 67555
 
8.0%
r 58639
 
7.0%
i 53387
 
6.3%
o 49815
 
5.9%
l 46184
 
5.5%
n 35360
 
4.2%
d 34945
 
4.1%
t 24230
 
2.9%
Other values (43) 262073
31.1%
Common
ValueCountFrequency (%)
43575
75.4%
- 13179
 
22.8%
1 504
 
0.9%
2 504
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 881146
97.8%
None 19587
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 134891
15.3%
e 75892
 
8.6%
s 67555
 
7.7%
r 58639
 
6.7%
i 53387
 
6.1%
o 49815
 
5.7%
l 46184
 
5.2%
43575
 
4.9%
n 35360
 
4.0%
d 34945
 
4.0%
Other values (39) 280903
31.9%
None
ValueCountFrequency (%)
ñ 4756
24.3%
í 4416
22.5%
é 4392
22.4%
ó 3327
17.0%
Á 939
 
4.8%
ü 681
 
3.5%
ú 668
 
3.4%
á 408
 
2.1%

buen_estado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
58433 
0
16166 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

Length

2024-02-28T08:51:28.803086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:28.886170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

Most occurring characters

ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 58433
78.3%
0 16166
 
21.7%

cat_ano_construccion
Real number (ℝ)

Distinct165
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1965.4264
Minimum1623
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:28.969455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1623
5-th percentile1900
Q11955
median1967
Q31983
95-th percentile2007
Maximum2018
Range395
Interquartile range (IQR)28

Descriptive statistics

Standard deviation29.042958
Coefficient of variation (CV)0.014776924
Kurtosis2.1179887
Mean1965.4264
Median Absolute Deviation (MAD)14
Skewness-0.82080908
Sum1.4661885 × 108
Variance843.49339
MonotonicityNot monotonic
2024-02-28T08:51:29.102625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1960 4738
 
6.4%
1965 3883
 
5.2%
1970 3704
 
5.0%
1900 2755
 
3.7%
1950 1737
 
2.3%
1940 1634
 
2.2%
1969 1568
 
2.1%
1968 1478
 
2.0%
1930 1450
 
1.9%
1966 1413
 
1.9%
Other values (155) 50239
67.3%
ValueCountFrequency (%)
1623 1
 
< 0.1%
1627 3
< 0.1%
1655 1
 
< 0.1%
1692 1
 
< 0.1%
1696 1
 
< 0.1%
1723 1
 
< 0.1%
1730 2
 
< 0.1%
1800 6
< 0.1%
1820 1
 
< 0.1%
1829 4
< 0.1%
ValueCountFrequency (%)
2018 623
0.8%
2017 341
0.5%
2016 112
 
0.2%
2015 110
 
0.1%
2014 296
0.4%
2013 119
 
0.2%
2012 143
 
0.2%
2011 143
 
0.2%
2010 349
0.5%
2009 472
0.6%

cat_calidad
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.8527172
Minimum0
Maximum9
Zeros310
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:29.202639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4605042
Coefficient of variation (CV)0.30096626
Kurtosis-0.023973809
Mean4.8527172
Median Absolute Deviation (MAD)1
Skewness-0.042383038
Sum362003
Variance2.1330724
MonotonicityNot monotonic
2024-02-28T08:51:29.285937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 19548
26.2%
5 16379
22.0%
6 16066
21.5%
3 9822
13.2%
7 8234
11.0%
2 2072
 
2.8%
8 1167
 
1.6%
1 508
 
0.7%
9 492
 
0.7%
0 310
 
0.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 310
 
0.4%
1 508
 
0.7%
2 2072
 
2.8%
3 9822
13.2%
4 19548
26.2%
5 16379
22.0%
6 16066
21.5%
7 8234
11.0%
8 1167
 
1.6%
9 492
 
0.7%
ValueCountFrequency (%)
9 492
 
0.7%
8 1167
 
1.6%
7 8234
11.0%
6 16066
21.5%
5 16379
22.0%
4 19548
26.2%
3 9822
13.2%
2 2072
 
2.8%
1 508
 
0.7%
0 310
 
0.4%

cat_n_max_pisos
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4009571
Minimum0
Maximum26
Zeros91
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:29.371994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8633404
Coefficient of variation (CV)0.44733004
Kurtosis5.9413972
Mean6.4009571
Median Absolute Deviation (MAD)1
Skewness1.7861134
Sum477505
Variance8.1987181
MonotonicityNot monotonic
2024-02-28T08:51:29.452692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 16730
22.4%
7 10811
14.5%
4 10810
14.5%
6 10782
14.5%
8 7455
10.0%
3 4243
 
5.7%
9 4137
 
5.5%
10 2255
 
3.0%
2 1371
 
1.8%
11 1273
 
1.7%
Other values (16) 4732
 
6.3%
ValueCountFrequency (%)
0 91
 
0.1%
1 456
 
0.6%
2 1371
 
1.8%
3 4243
 
5.7%
4 10810
14.5%
5 16730
22.4%
6 10782
14.5%
7 10811
14.5%
8 7455
10.0%
9 4137
 
5.5%
ValueCountFrequency (%)
26 39
 
0.1%
25 26
 
< 0.1%
23 69
 
0.1%
22 63
 
0.1%
21 155
0.2%
20 74
 
0.1%
19 11
 
< 0.1%
18 70
 
0.1%
17 243
0.3%
16 189
0.3%

cat_n_vecinos
Real number (ℝ)

Distinct324
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.23318
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:29.552724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median21
Q340
95-th percentile140
Maximum1499
Range1498
Interquartile range (IQR)28

Descriptive statistics

Standard deviation54.305578
Coefficient of variation (CV)1.3841748
Kurtosis31.400544
Mean39.23318
Median Absolute Deviation (MAD)11
Skewness4.2744404
Sum2926756
Variance2949.0958
MonotonicityNot monotonic
2024-02-28T08:51:29.669549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3847
 
5.2%
21 3218
 
4.3%
9 3135
 
4.2%
13 2654
 
3.6%
17 2233
 
3.0%
16 2153
 
2.9%
7 1982
 
2.7%
15 1925
 
2.6%
14 1915
 
2.6%
10 1907
 
2.6%
Other values (314) 49630
66.5%
ValueCountFrequency (%)
1 552
 
0.7%
2 812
 
1.1%
3 829
 
1.1%
4 1059
 
1.4%
5 1152
 
1.5%
6 850
 
1.1%
7 1982
2.7%
8 1250
 
1.7%
9 3135
4.2%
10 1907
2.6%
ValueCountFrequency (%)
1499 1
 
< 0.1%
724 18
 
< 0.1%
638 3
 
< 0.1%
574 47
0.1%
518 1
 
< 0.1%
512 1
 
< 0.1%
503 9
 
< 0.1%
501 5
 
< 0.1%
478 17
 
< 0.1%
462 16
 
< 0.1%

ciudad
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Madrid
74599 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters447594
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadrid
2nd rowMadrid
3rd rowMadrid
4th rowMadrid
5th rowMadrid

Common Values

ValueCountFrequency (%)
Madrid 74599
100.0%

Length

2024-02-28T08:51:29.769334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:29.837933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
madrid 74599
100.0%

Most occurring characters

ValueCountFrequency (%)
d 149198
33.3%
M 74599
16.7%
a 74599
16.7%
r 74599
16.7%
i 74599
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 372995
83.3%
Uppercase Letter 74599
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 149198
40.0%
a 74599
20.0%
r 74599
20.0%
i 74599
20.0%
Uppercase Letter
ValueCountFrequency (%)
M 74599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 447594
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 149198
33.3%
M 74599
16.7%
a 74599
16.7%
r 74599
16.7%
i 74599
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 447594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 149198
33.3%
M 74599
16.7%
a 74599
16.7%
r 74599
16.7%
i 74599
16.7%

distancia_castellana
Real number (ℝ)

Distinct74514
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.598035
Minimum0.0014350974
Maximum12.553787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:29.919428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014350974
5-th percentile0.27274734
Q11.027001
median1.9158986
Q33.7334686
95-th percentile6.8163452
Maximum12.553787
Range12.552352
Interquartile range (IQR)2.7064676

Descriptive statistics

Standard deviation2.1191431
Coefficient of variation (CV)0.81567149
Kurtosis1.2293981
Mean2.598035
Median Absolute Deviation (MAD)1.1519427
Skewness1.2337361
Sum193810.81
Variance4.4907674
MonotonicityNot monotonic
2024-02-28T08:51:30.035853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.856961844 2
 
< 0.1%
1.594802311 2
 
< 0.1%
1.198837622 2
 
< 0.1%
0.395478197 2
 
< 0.1%
1.363194516 2
 
< 0.1%
1.067497226 2
 
< 0.1%
1.151967651 2
 
< 0.1%
9.125332602 2
 
< 0.1%
5.340889233 2
 
< 0.1%
0.2787901865 2
 
< 0.1%
Other values (74504) 74579
> 99.9%
ValueCountFrequency (%)
0.001435097407 1
< 0.1%
0.004322044394 1
< 0.1%
0.004934849361 1
< 0.1%
0.006173318995 1
< 0.1%
0.007929002888 1
< 0.1%
0.008344202002 1
< 0.1%
0.008422856558 1
< 0.1%
0.008501845916 1
< 0.1%
0.008559026743 1
< 0.1%
0.008597461344 1
< 0.1%
ValueCountFrequency (%)
12.5537872 1
< 0.1%
12.55331639 1
< 0.1%
12.53622285 1
< 0.1%
12.53313122 1
< 0.1%
12.51061628 1
< 0.1%
12.50074993 1
< 0.1%
12.47485373 1
< 0.1%
12.43771592 1
< 0.1%
12.39198962 1
< 0.1%
12.30004504 1
< 0.1%

distancia_metro
Real number (ℝ)

Distinct74347
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44044939
Minimum0.0014160887
Maximum8.889995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:30.152591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0014160887
5-th percentile0.095973421
Q10.21236562
median0.32838547
Q30.51392704
95-th percentile1.1114601
Maximum8.889995
Range8.8885789
Interquartile range (IQR)0.30156142

Descriptive statistics

Standard deviation0.45543265
Coefficient of variation (CV)1.0340181
Kurtosis50.396726
Mean0.44044939
Median Absolute Deviation (MAD)0.13726983
Skewness5.5737984
Sum32857.084
Variance0.2074189
MonotonicityNot monotonic
2024-02-28T08:51:30.252325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1351915238 3
 
< 0.1%
0.03547740611 3
 
< 0.1%
0.1463972129 2
 
< 0.1%
0.548435256 2
 
< 0.1%
0.0451726862 2
 
< 0.1%
0.1888043893 2
 
< 0.1%
0.08628305605 2
 
< 0.1%
0.07869362329 2
 
< 0.1%
0.05674742503 2
 
< 0.1%
0.2067488781 2
 
< 0.1%
Other values (74337) 74577
> 99.9%
ValueCountFrequency (%)
0.001416088655 1
< 0.1%
0.003159330755 1
< 0.1%
0.004017688228 1
< 0.1%
0.004132945902 1
< 0.1%
0.004134038551 1
< 0.1%
0.004135130912 1
< 0.1%
0.004376046082 1
< 0.1%
0.004477056832 1
< 0.1%
0.004687972017 1
< 0.1%
0.004969505909 1
< 0.1%
ValueCountFrequency (%)
8.889994953 1
< 0.1%
7.820931317 1
< 0.1%
6.79841124 1
< 0.1%
6.755273484 1
< 0.1%
6.737781821 1
< 0.1%
6.725736225 1
< 0.1%
6.686344961 1
< 0.1%
6.472492457 1
< 0.1%
6.422737829 1
< 0.1%
6.418830016 1
< 0.1%

distancia_puerta_sol
Real number (ℝ)

Distinct74518
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4218064
Minimum0.0076465716
Maximum14.139526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:30.335633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.0076465716
5-th percentile0.75746164
Q12.3885494
median4.0651236
Q36.091445
95-th percentile9.0787015
Maximum14.139526
Range14.131879
Interquartile range (IQR)3.7028956

Descriptive statistics

Standard deviation2.6384345
Coefficient of variation (CV)0.59668701
Kurtosis-0.15071002
Mean4.4218064
Median Absolute Deviation (MAD)1.79744
Skewness0.60479098
Sum329862.34
Variance6.9613364
MonotonicityNot monotonic
2024-02-28T08:51:30.752254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14748725 2
 
< 0.1%
3.198448428 2
 
< 0.1%
1.633135538 2
 
< 0.1%
2.435360596 2
 
< 0.1%
0.1848192302 2
 
< 0.1%
0.9410136263 2
 
< 0.1%
0.1400464396 2
 
< 0.1%
1.951229271 2
 
< 0.1%
4.472828629 2
 
< 0.1%
2.017072721 2
 
< 0.1%
Other values (74508) 74579
> 99.9%
ValueCountFrequency (%)
0.007646571605 1
< 0.1%
0.01703500572 1
< 0.1%
0.01994969235 1
< 0.1%
0.02541720017 1
< 0.1%
0.02546122988 1
< 0.1%
0.02558615768 1
< 0.1%
0.02911750986 1
< 0.1%
0.03200618324 1
< 0.1%
0.03215794221 1
< 0.1%
0.03268092424 1
< 0.1%
ValueCountFrequency (%)
14.13952592 1
< 0.1%
14.13033589 1
< 0.1%
14.12090709 1
< 0.1%
14.11331405 1
< 0.1%
14.09935996 1
< 0.1%
14.08839696 1
< 0.1%
14.05900163 1
< 0.1%
14.0234639 1
< 0.1%
13.98417566 1
< 0.1%
13.47477246 1
< 0.1%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72649 
1
 
1950

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

Length

2024-02-28T08:51:30.867933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:30.935582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72649
97.4%
1 1950
 
2.6%

estudio
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72539 
1
 
2060

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

Length

2024-02-28T08:51:31.018839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:31.101994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72539
97.2%
1 2060
 
2.8%

exterior_interior
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4996
Missing (%)6.7%
Memory size1.1 MiB
1.0
60035 
2.0
9568 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters208809
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 60035
80.5%
2.0 9568
 
12.8%
(Missing) 4996
 
6.7%

Length

2024-02-28T08:51:31.208264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:31.287326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 60035
86.3%
2.0 9568
 
13.7%

Most occurring characters

ValueCountFrequency (%)
. 69603
33.3%
0 69603
33.3%
1 60035
28.8%
2 9568
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 139206
66.7%
Other Punctuation 69603
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 69603
50.0%
1 60035
43.1%
2 9568
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 69603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 208809
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 69603
33.3%
0 69603
33.3%
1 60035
28.8%
2 9568
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 208809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 69603
33.3%
0 69603
33.3%
1 60035
28.8%
2 9568
 
4.6%

fecha
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
201812
34736 
201803
17277 
201809
12611 
201806
9975 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters447594
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201803
2nd row201812
3rd row201812
4th row201812
5th row201812

Common Values

ValueCountFrequency (%)
201812 34736
46.6%
201803 17277
23.2%
201809 12611
 
16.9%
201806 9975
 
13.4%

Length

2024-02-28T08:51:31.386424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:31.469229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
201812 34736
46.6%
201803 17277
23.2%
201809 12611
 
16.9%
201806 9975
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 114462
25.6%
2 109335
24.4%
1 109335
24.4%
8 74599
16.7%
3 17277
 
3.9%
9 12611
 
2.8%
6 9975
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 447594
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 114462
25.6%
2 109335
24.4%
1 109335
24.4%
8 74599
16.7%
3 17277
 
3.9%
9 12611
 
2.8%
6 9975
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 447594
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 114462
25.6%
2 109335
24.4%
1 109335
24.4%
8 74599
16.7%
3 17277
 
3.9%
9 12611
 
2.8%
6 9975
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 447594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 114462
25.6%
2 109335
24.4%
1 109335
24.4%
8 74599
16.7%
3 17277
 
3.9%
9 12611
 
2.8%
6 9975
 
2.2%
Distinct62173
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:31.637602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length20
Mean length20.392834
Min length17

Characters and Unicode

Total characters1521285
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52547 ?
Unique (%)70.4%

Sample

1st rowA15158251754379462437
2nd rowA14202580375036702435
3rd rowA17523340311988477659
4th rowA384463467707172686
5th rowA17571909207595892279
ValueCountFrequency (%)
a14882068007191593522 9
 
< 0.1%
a1315840462730187222 7
 
< 0.1%
a5463639993615125363 7
 
< 0.1%
a2282202115281541721 7
 
< 0.1%
a9858360437524013306 7
 
< 0.1%
a9865685988976540204 7
 
< 0.1%
a5953256861383778054 7
 
< 0.1%
a15887017636239933239 7
 
< 0.1%
a14940791098683555615 6
 
< 0.1%
a8420133463712996809 6
 
< 0.1%
Other values (62163) 74529
99.9%
2024-02-28T08:51:31.938019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 176829
11.6%
3 143049
9.4%
2 142888
9.4%
4 142302
9.4%
5 142278
9.4%
7 142078
9.3%
6 141868
9.3%
8 139767
9.2%
0 137905
9.1%
9 137722
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1446686
95.1%
Uppercase Letter 74599
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 176829
12.2%
3 143049
9.9%
2 142888
9.9%
4 142302
9.8%
5 142278
9.8%
7 142078
9.8%
6 141868
9.8%
8 139767
9.7%
0 137905
9.5%
9 137722
9.5%
Uppercase Letter
ValueCountFrequency (%)
A 74599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1446686
95.1%
Latin 74599
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 176829
12.2%
3 143049
9.9%
2 142888
9.9%
4 142302
9.8%
5 142278
9.8%
7 142078
9.8%
6 141868
9.8%
8 139767
9.7%
0 137905
9.5%
9 137722
9.5%
Latin
ValueCountFrequency (%)
A 74599
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1521285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 176829
11.6%
3 143049
9.4%
2 142888
9.4%
4 142302
9.4%
5 142278
9.4%
7 142078
9.3%
6 141868
9.3%
8 139767
9.2%
0 137905
9.1%
9 137722
9.1%

jardin
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
61129 
1
13470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

Length

2024-02-28T08:51:32.053878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:32.118618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61129
81.9%
1 13470
 
18.1%

latitud
Real number (ℝ)

Distinct74518
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.421035
Minimum40.331652
Maximum40.520637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:32.217930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40.331652
5-th percentile40.36704
Q140.396912
median40.423286
Q340.441397
95-th percentile40.476783
Maximum40.520637
Range0.18898485
Interquartile range (IQR)0.044484896

Descriptive statistics

Standard deviation0.033405281
Coefficient of variation (CV)0.00082643309
Kurtosis-0.23961715
Mean40.421035
Median Absolute Deviation (MAD)0.022370034
Skewness0.035841359
Sum3015368.8
Variance0.0011159128
MonotonicityNot monotonic
2024-02-28T08:51:32.318860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.42515983 2
 
< 0.1%
40.4266461 2
 
< 0.1%
40.40218821 2
 
< 0.1%
40.42592491 2
 
< 0.1%
40.4158119 2
 
< 0.1%
40.42419541 2
 
< 0.1%
40.41553178 2
 
< 0.1%
40.40226391 2
 
< 0.1%
40.45658557 2
 
< 0.1%
40.40137903 2
 
< 0.1%
Other values (74508) 74579
> 99.9%
ValueCountFrequency (%)
40.33165199 1
< 0.1%
40.33169949 1
< 0.1%
40.33212122 1
< 0.1%
40.33212891 1
< 0.1%
40.33214619 1
< 0.1%
40.33241042 1
< 0.1%
40.33249129 1
< 0.1%
40.33252415 1
< 0.1%
40.33282418 1
< 0.1%
40.33291801 1
< 0.1%
ValueCountFrequency (%)
40.52063684 1
< 0.1%
40.51983257 1
< 0.1%
40.51908625 1
< 0.1%
40.5189964 1
< 0.1%
40.51871465 1
< 0.1%
40.51864171 1
< 0.1%
40.51763185 1
< 0.1%
40.51706304 1
< 0.1%
40.51676269 1
< 0.1%
40.5166948 1
< 0.1%

longitud
Real number (ℝ)

Distinct74518
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6858326
Minimum-3.826629
Maximum-3.541071
Zeros0
Zeros (%)0.0%
Negative74599
Negative (%)100.0%
Memory size1.1 MiB
2024-02-28T08:51:32.435502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3.826629
5-th percentile-3.7436394
Q1-3.7079536
median-3.6937005
Q3-3.6662812
95-th percentile-3.6131843
Maximum-3.541071
Range0.285558
Interquartile range (IQR)0.041672422

Descriptive statistics

Standard deviation0.037544062
Coefficient of variation (CV)-0.010186046
Kurtosis0.54115213
Mean-3.6858326
Median Absolute Deviation (MAD)0.020481365
Skewness0.4845682
Sum-274959.43
Variance0.0014095566
MonotonicityNot monotonic
2024-02-28T08:51:32.535210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.668397562 2
 
< 0.1%
-3.668435853 2
 
< 0.1%
-3.700117871 2
 
< 0.1%
-3.677800626 2
 
< 0.1%
-3.70571635 2
 
< 0.1%
-3.698943196 2
 
< 0.1%
-3.702896392 2
 
< 0.1%
-3.690526127 2
 
< 0.1%
-3.708843606 2
 
< 0.1%
-3.690858829 2
 
< 0.1%
Other values (74508) 74579
> 99.9%
ValueCountFrequency (%)
-3.826628998 1
< 0.1%
-3.813004567 1
< 0.1%
-3.802893259 1
< 0.1%
-3.802322109 1
< 0.1%
-3.8022819 1
< 0.1%
-3.799809612 1
< 0.1%
-3.799252676 1
< 0.1%
-3.798004486 1
< 0.1%
-3.797244343 1
< 0.1%
-3.796863623 1
< 0.1%
ValueCountFrequency (%)
-3.541071002 1
< 0.1%
-3.541146808 1
< 0.1%
-3.541271311 1
< 0.1%
-3.541407161 1
< 0.1%
-3.541678655 1
< 0.1%
-3.541784055 1
< 0.1%
-3.542052944 1
< 0.1%
-3.542501527 1
< 0.1%
-3.543094218 1
< 0.1%
-3.547034608 1
< 0.1%

n_banos
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5813081
Minimum0
Maximum20
Zeros60
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:32.618563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83961872
Coefficient of variation (CV)0.53096468
Kurtosis15.495243
Mean1.5813081
Median Absolute Deviation (MAD)0
Skewness2.4858612
Sum117964
Variance0.7049596
MonotonicityNot monotonic
2024-02-28T08:51:32.702026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 42279
56.7%
2 24693
33.1%
3 5182
 
6.9%
4 1629
 
2.2%
5 547
 
0.7%
6 123
 
0.2%
0 60
 
0.1%
7 33
 
< 0.1%
8 20
 
< 0.1%
11 14
 
< 0.1%
Other values (7) 19
 
< 0.1%
ValueCountFrequency (%)
0 60
 
0.1%
1 42279
56.7%
2 24693
33.1%
3 5182
 
6.9%
4 1629
 
2.2%
5 547
 
0.7%
6 123
 
0.2%
7 33
 
< 0.1%
8 20
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 14
< 0.1%
10 6
 
< 0.1%
9 5
 
< 0.1%
8 20
< 0.1%
7 33
< 0.1%

n_habitaciones
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5774742
Minimum0
Maximum93
Zeros2155
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:32.801948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum93
Range93
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2574558
Coefficient of variation (CV)0.48786357
Kurtosis367.224
Mean2.5774742
Median Absolute Deviation (MAD)1
Skewness5.951532
Sum192277
Variance1.581195
MonotonicityNot monotonic
2024-02-28T08:51:32.885011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 26542
35.6%
2 22437
30.1%
1 10606
 
14.2%
4 9191
 
12.3%
5 2614
 
3.5%
0 2155
 
2.9%
6 634
 
0.8%
7 219
 
0.3%
8 106
 
0.1%
9 28
 
< 0.1%
Other values (11) 67
 
0.1%
ValueCountFrequency (%)
0 2155
 
2.9%
1 10606
 
14.2%
2 22437
30.1%
3 26542
35.6%
4 9191
 
12.3%
5 2614
 
3.5%
6 634
 
0.8%
7 219
 
0.3%
8 106
 
0.1%
9 28
 
< 0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
33 1
 
< 0.1%
20 2
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 4
 
< 0.1%
13 4
 
< 0.1%
12 14
< 0.1%
11 13
< 0.1%

n_piso
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing3006
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean2.7577417
Minimum-1
Maximum11
Zeros7852
Zeros (%)10.5%
Negative746
Negative (%)1.0%
Memory size1.1 MiB
2024-02-28T08:51:32.965177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum11
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2642142
Coefficient of variation (CV)0.82103927
Kurtosis1.6113034
Mean2.7577417
Median Absolute Deviation (MAD)1
Skewness1.1631652
Sum197435
Variance5.126666
MonotonicityNot monotonic
2024-02-28T08:51:33.034964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 16033
21.5%
2 13349
17.9%
3 11621
15.6%
4 9220
12.4%
0 7852
10.5%
5 4867
 
6.5%
6 2936
 
3.9%
7 1940
 
2.6%
8 1166
 
1.6%
11 861
 
1.2%
Other values (3) 1748
 
2.3%
(Missing) 3006
 
4.0%
ValueCountFrequency (%)
-1 746
 
1.0%
0 7852
10.5%
1 16033
21.5%
2 13349
17.9%
3 11621
15.6%
4 9220
12.4%
5 4867
 
6.5%
6 2936
 
3.9%
7 1940
 
2.6%
8 1166
 
1.6%
ValueCountFrequency (%)
11 861
 
1.2%
10 359
 
0.5%
9 643
 
0.9%
8 1166
 
1.6%
7 1940
 
2.6%
6 2936
 
3.9%
5 4867
 
6.5%
4 9220
12.4%
3 11621
15.6%
2 13349
17.9%

nueva_construccion
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
72415 
1
 
2184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

Length

2024-02-28T08:51:33.135097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.201650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 72415
97.1%
1 2184
 
2.9%

operacion
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
SALE
74599 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters298396
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSALE
2nd rowSALE
3rd rowSALE
4th rowSALE
5th rowSALE

Common Values

ValueCountFrequency (%)
SALE 74599
100.0%

Length

2024-02-28T08:51:33.284868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.365338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
sale 74599
100.0%

Most occurring characters

ValueCountFrequency (%)
S 74599
25.0%
A 74599
25.0%
L 74599
25.0%
E 74599
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 298396
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 74599
25.0%
A 74599
25.0%
L 74599
25.0%
E 74599
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 298396
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 74599
25.0%
A 74599
25.0%
L 74599
25.0%
E 74599
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 74599
25.0%
A 74599
25.0%
L 74599
25.0%
E 74599
25.0%

orientacion_e
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
59502 
1
15097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

Length

2024-02-28T08:51:33.434806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.518340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59502
79.8%
1 15097
 
20.2%

orientacion_n
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
66565 
1
8034 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

Length

2024-02-28T08:51:33.601510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.668627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66565
89.2%
1 8034
 
10.8%

orientacion_o
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
63539 
1
11060 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

Length

2024-02-28T08:51:33.760221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.834775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63539
85.2%
1 11060
 
14.8%

orientacion_s
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
57017 
1
17582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

Length

2024-02-28T08:51:33.918140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:33.984814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57017
76.4%
1 17582
 
23.6%

parking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
58042 
1
16557 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Length

2024-02-28T08:51:34.068039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:34.134707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
58042 
1
16557 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Length

2024-02-28T08:51:34.217992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:34.301143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58042
77.8%
1 16557
 
22.2%

piscina
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
63895 
1
10704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

Length

2024-02-28T08:51:34.384689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:34.464329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63895
85.7%
1 10704
 
14.3%

portero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
55950 
1
18649 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

Length

2024-02-28T08:51:34.534736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:34.634449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

Most occurring characters

ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55950
75.0%
1 18649
 
25.0%

precio
Real number (ℝ)

Distinct2619
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396872.57
Minimum21000
Maximum8133000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:34.717798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21000
5-th percentile97000
Q1160000
median263000
Q3468500
95-th percentile1139000
Maximum8133000
Range8112000
Interquartile range (IQR)308500

Descriptive statistics

Standard deviation419438.36
Coefficient of variation (CV)1.056859
Kurtosis29.358699
Mean396872.57
Median Absolute Deviation (MAD)124000
Skewness4.0663951
Sum2.9606297 × 1010
Variance1.7592854 × 1011
MonotonicityNot monotonic
2024-02-28T08:51:34.834369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137000 351
 
0.5%
127000 337
 
0.5%
132000 305
 
0.4%
128000 304
 
0.4%
138000 303
 
0.4%
158000 296
 
0.4%
130000 293
 
0.4%
147000 289
 
0.4%
119000 289
 
0.4%
162000 286
 
0.4%
Other values (2609) 71546
95.9%
ValueCountFrequency (%)
21000 1
 
< 0.1%
24000 3
< 0.1%
26000 1
 
< 0.1%
28000 1
 
< 0.1%
29000 3
< 0.1%
30000 1
 
< 0.1%
32000 1
 
< 0.1%
33000 3
< 0.1%
35000 2
< 0.1%
36000 2
< 0.1%
ValueCountFrequency (%)
8133000 1
< 0.1%
7138000 1
< 0.1%
7044000 1
< 0.1%
7018000 1
< 0.1%
6996000 1
< 0.1%
6970000 1
< 0.1%
6848000 1
< 0.1%
6729000 1
< 0.1%
6702000 1
< 0.1%
6642000 1
< 0.1%

precio_logaritmico
Real number (ℝ)

Distinct2619
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.564713
Minimum9.9522777
Maximum15.91144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:34.954744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.9522777
5-th percentile11.482466
Q111.982929
median12.479909
Q313.057291
95-th percentile13.945661
Maximum15.91144
Range5.9591627
Interquartile range (IQR)1.0743617

Descriptive statistics

Standard deviation0.76087021
Coefficient of variation (CV)0.060556117
Kurtosis0.040415066
Mean12.564713
Median Absolute Deviation (MAD)0.52872892
Skewness0.52960517
Sum937315
Variance0.57892348
MonotonicityNot monotonic
2024-02-28T08:51:35.057850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8277362 351
 
0.5%
11.75194237 337
 
0.5%
11.7905572 305
 
0.4%
11.75978554 304
 
0.4%
11.83500896 303
 
0.4%
11.97035031 296
 
0.4%
11.77528973 293
 
0.4%
11.89818787 289
 
0.4%
11.68687877 289
 
0.4%
11.99535161 286
 
0.4%
Other values (2609) 71546
95.9%
ValueCountFrequency (%)
9.952277717 1
 
< 0.1%
10.08580911 3
< 0.1%
10.16585182 1
 
< 0.1%
10.23995979 1
 
< 0.1%
10.27505111 3
< 0.1%
10.30895266 1
 
< 0.1%
10.37349118 1
 
< 0.1%
10.40426284 3
< 0.1%
10.46310334 2
< 0.1%
10.49127422 2
< 0.1%
ValueCountFrequency (%)
15.91144042 1
< 0.1%
15.78094318 1
< 0.1%
15.76768675 1
< 0.1%
15.76398884 1
< 0.1%
15.76084912 1
< 0.1%
15.75712578 1
< 0.1%
15.7394672 1
< 0.1%
15.7219371 1
< 0.1%
15.71791655 1
< 0.1%
15.70892368 1
< 0.1%

precio_parking
Real number (ℝ)

SKEWED 

Distinct135
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean737.27385
Minimum1
Maximum925001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:35.162013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum925001
Range925000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7965.034
Coefficient of variation (CV)10.803359
Kurtosis5072.426
Mean737.27385
Median Absolute Deviation (MAD)0
Skewness54.35967
Sum54999892
Variance63441766
MonotonicityNot monotonic
2024-02-28T08:51:35.264795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 72842
97.6%
20001 186
 
0.2%
30001 166
 
0.2%
25001 151
 
0.2%
15001 137
 
0.2%
40001 119
 
0.2%
50001 105
 
0.1%
45001 67
 
0.1%
35001 55
 
0.1%
60001 53
 
0.1%
Other values (125) 718
 
1.0%
ValueCountFrequency (%)
1 72842
97.6%
2 5
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
26 2
 
< 0.1%
41 2
 
< 0.1%
51 4
 
< 0.1%
ValueCountFrequency (%)
925001 1
< 0.1%
770001 1
< 0.1%
750001 1
< 0.1%
510001 1
< 0.1%
450001 1
< 0.1%
275001 1
< 0.1%
231001 2
< 0.1%
220001 1
< 0.1%
150001 2
< 0.1%
125001 1
< 0.1%

precio_unitario_m2
Real number (ℝ)

Distinct27304
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3670.9872
Minimum805.55556
Maximum9997.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-02-28T08:51:35.362699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum805.55556
5-th percentile1427.0833
Q12246.621
median3491.2281
Q34757.7352
95-th percentile6788.9474
Maximum9997.561
Range9192.0054
Interquartile range (IQR)2511.1142

Descriptive statistics

Standard deviation1705.5818
Coefficient of variation (CV)0.4646112
Kurtosis0.21713922
Mean3670.9872
Median Absolute Deviation (MAD)1253.6699
Skewness0.71601405
Sum2.7385197 × 108
Variance2909009.2
MonotonicityNot monotonic
2024-02-28T08:51:35.464837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 294
 
0.4%
3000 186
 
0.2%
5000 166
 
0.2%
4000 155
 
0.2%
2200 125
 
0.2%
1800 120
 
0.2%
3500 117
 
0.2%
1500 116
 
0.2%
2500 113
 
0.2%
2600 106
 
0.1%
Other values (27294) 73101
98.0%
ValueCountFrequency (%)
805.5555556 1
< 0.1%
805.9701493 1
< 0.1%
806.4516129 1
< 0.1%
807.0175439 1
< 0.1%
808.3333333 1
< 0.1%
808.8235294 1
< 0.1%
809.0909091 1
< 0.1%
810.6060606 1
< 0.1%
812.5 1
< 0.1%
815.3846154 1
< 0.1%
ValueCountFrequency (%)
9997.560976 1
< 0.1%
9994.285714 1
< 0.1%
9993.377483 1
< 0.1%
9992.248062 1
< 0.1%
9990.909091 1
< 0.1%
9984.924623 1
< 0.1%
9979.310345 1
< 0.1%
9975 1
< 0.1%
9973.913043 1
< 0.1%
9967.741935 1
< 0.1%

terraza
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
48202 
1
26397 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

Length

2024-02-28T08:51:35.558133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:35.634224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

Most occurring characters

ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48202
64.6%
1 26397
35.4%

tipologia_imueble
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
HOME
74599 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters298396
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOME
2nd rowHOME
3rd rowHOME
4th rowHOME
5th rowHOME

Common Values

ValueCountFrequency (%)
HOME 74599
100.0%

Length

2024-02-28T08:51:35.718540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:35.784279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
home 74599
100.0%

Most occurring characters

ValueCountFrequency (%)
H 74599
25.0%
O 74599
25.0%
M 74599
25.0%
E 74599
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 298396
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 74599
25.0%
O 74599
25.0%
M 74599
25.0%
E 74599
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 298396
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 74599
25.0%
O 74599
25.0%
M 74599
25.0%
E 74599
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 74599
25.0%
O 74599
25.0%
M 74599
25.0%
E 74599
25.0%

trastero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
55557 
1
19042 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters74599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Length

2024-02-28T08:51:35.868083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T08:51:35.950840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 74599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55557
74.5%
1 19042
 
25.5%

Interactions

2024-02-28T08:51:23.764318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:55.714978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.447456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.246602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.929739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.514529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.254184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.841629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.386039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.091762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.692319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.321239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.971487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.486674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.974425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.558215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.172155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.144142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.857648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:55.835410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.554601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.348147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.016062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.603287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.338861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.925862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.460306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.163694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.766505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.399645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.057514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.570886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.058093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.658369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.240699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.278861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.950723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:55.914333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.684401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.448073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.100930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.680460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.423939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.004335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.541389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.243867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.859055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.473084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.144217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.643928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.125906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.740063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.329864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.373428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.048171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:55.998420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.764571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.530608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.178743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.765715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.523776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.098502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.631473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.343028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.968107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.568498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.223939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.723772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.226213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.874856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.424137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.473743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.132116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.085748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.847282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.630267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.244891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.851430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.594256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.182005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.712395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.434853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.067220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.644855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.298593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.802086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.309261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.958036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.509112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.540647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.220700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.183224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.950298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.725174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.330856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.935941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.672067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.273827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.776135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.517729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.178407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.721957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.386809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.870682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.390358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.041452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.591147image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.631322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.316484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.300992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.064602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.816155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.417230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.020242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.778350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.368931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.014917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.595969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.276731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.800407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.471144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.955379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.493437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.128538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.696132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.725160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.414601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.408427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.191207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.898991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.495738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.221195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.875669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.447001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.114865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.680408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.359941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.884786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.558961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.041873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.575445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.206241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.041743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.806601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.514287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.506486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.285309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.977189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.585587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.305523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.940365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.527485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.192139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.776268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.451678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.959157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.642833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.117143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.658226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.289645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.128535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.873082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.615270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.582876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.365270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.083973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.667365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.398558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.040347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.611664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.269797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.859958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.536273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.038301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.724340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.209026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.758868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.390713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.210442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.971936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.723937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.680754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.482812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.178339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.748671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.501997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.138946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.712064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.380714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.949679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.636288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.143664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.824225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.291610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.858590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.489364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.310542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.054814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.805951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.763951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.563451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.261710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.853499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.606905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.256649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.797035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.461673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.045684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.720444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.206084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.902643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.396290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:17.974155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.572609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.423888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.138362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:24.938640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.851615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.665081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.373085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:01.964829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.713292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.360394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.878739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.546365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.144765image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.814477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.302540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:14.986863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.495187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.078238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.672696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.506598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.221531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:25.024834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:56.989352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.730375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.460726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.079247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.788115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.440808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:06.976135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.628960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.234253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.893433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.370117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.071276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.574960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.158538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.756074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.598372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.304965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:25.122755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.077044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.813865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.554467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.165137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:03.911992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.525687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.059990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.747450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.318665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:11.983224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.454263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.140770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.659847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.229083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.829313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.702770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.388016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:25.205500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.163610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.898256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.664768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.270015image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.014992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.610108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.142602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.860794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.396786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.067507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.538653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.239953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.750479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.323412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.907067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.812448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.505101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:25.287582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.247089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:58.980499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.763609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.352093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.106879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.693237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.213008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:08.947371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.481820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.160463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.823166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.323808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.831528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.411461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:19.991370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:21.943699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.587992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:25.370908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:57.345101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:50:59.065255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:00.846594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:02.435170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:04.179136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:05.772952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:07.304895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:09.031434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:10.558662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:12.236884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:13.898256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:15.402535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:16.908942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:18.491646image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:20.074243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:22.038931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T08:51:23.671267image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-28T08:51:25.538502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T08:51:26.104004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

a_reformaraire_acondicionadoamuebladoano_construccionarea_construidaaricoarmariosascensorbarriobuen_estadocat_ano_construccioncat_calidadcat_n_max_pisoscat_n_vecinosciudaddistancia_castellanadistancia_metrodistancia_puerta_solduplexestudioexterior_interiorfechaid_anunciojardinlatitudlongitudn_banosn_habitacionesn_pisonueva_construccionoperacionorientacion_eorientacion_norientacion_oorientacion_sparkingparking_incluido_preciopiscinaporteroprecioprecio_logaritmicoprecio_parkingprecio_unitario_m2terrazatipologia_imuebletrastero
143540132008.065011Cuatro Vientos120086.0916Madrid6.8575571.5108097.566884111.0201803A15158251754379462437140.373214-3.772508200.00SALE0000000115000011.91839112307.6923081HOME0
74496103NaN70000Casa de Campo019646.0422Madrid3.7765110.1418314.082331001.0201812A14202580375036702435040.392042-3.739567131.00SALE0000000012100011.70354611728.5714290HOME0
804850131981.0137011Fuente del Berro119814.014202Madrid2.7462310.2918483.839809001.0201812A17523340311988477659140.409554-3.659439231.00SALE0000110147500013.07107013467.1532851HOME1
83361003NaN62011Casa de Campo119796.0620Madrid2.3467520.0779763.657473001.0201812A384463467707172686040.386881-3.722215122.00SALE0000000015500011.95118012500.0000001HOME0
67119003NaN95001Huertas-Cortes019625.0817Madrid0.6624910.2494871.533640001.0201812A17571909207595892279140.429502-3.697477220.01SALE0000001071500013.48003817526.3157890HOME1
64911013NaN88011Ventas120054.08114Madrid2.9280891.6148709.888234001.0201809A9757537923896397319140.497760-3.656372223.00SALE0000111137900012.84529114306.8181820HOME1
3146013NaN45011Pacífico119881.0614Madrid0.8608240.0460252.526165002.0201803A4043234446226184768040.429394-3.679179121.00SALE0000000038400012.85839818533.3333330HOME0
650090132007.048011Sol120076.0737Madrid5.3434210.8142218.161336001.0201809A16967757291427247500040.343279-3.702782112.00SALE0000111113200011.79055712750.0000000HOME1
52043103NaN46000Ibiza019325.0521Madrid1.3552130.1765472.916737001.0201812A3527815958285728000040.428852-3.673374121.00SALE0001000024300012.40081715282.6086960HOME0
75152103NaN45000Palacio019714.0746Madrid1.5413500.5059043.951844002.0201806A17339574182937245195040.451873-3.708936120.00SALE0000000010600011.57119412355.5555560HOME0
a_reformaraire_acondicionadoamuebladoano_construccionarea_construidaaricoarmariosascensorbarriobuen_estadocat_ano_construccioncat_calidadcat_n_max_pisoscat_n_vecinosciudaddistancia_castellanadistancia_metrodistancia_puerta_solduplexestudioexterior_interiorfechaid_anunciojardinlatitudlongitudn_banosn_habitacionesn_pisonueva_construccionoperacionorientacion_eorientacion_norientacion_oorientacion_sparkingparking_incluido_preciopiscinaporteroprecioprecio_logaritmicoprecio_parkingprecio_unitario_m2terrazatipologia_imuebletrastero
60263003NaN46001Arapiles119966.0414Madrid1.4290820.3784363.83374200NaN201812A18242470779904893786040.450899-3.707722113.00SALE0000000015300011.93819313326.0869570HOME0
44131013NaN46101Lista119484.0641Madrid1.3755220.2203512.273511012.0201812A15095507513135919524040.416840-3.676967104.00SALE1010000028800012.57071616260.8695650HOME0
874980031992.090011Pueblo Nuevo119925.0418Madrid3.8834950.3028085.683917001.0201812A1587290933437902387040.438402-3.643143221.00SALE1000110029100012.58107913233.3333330HOME1
371940122010.0222011Arapiles120103.0512Madrid1.3787510.0779530.547478001.0201812A16254955556339487779040.417614-3.710108355.00SALE00010000115300013.95787815193.6936941HOME0
823860131998.0211011Casa de Campo119984.046Madrid3.8320560.1257414.283551101.0201812A2012864384796635965040.389929-3.740227242.00SALE0110110034800012.75995811649.2891001HOME1
62650031968.082000Casa de Campo119687.0713Madrid4.5312100.3977765.30006800NaN201803A16322158521314573955040.382406-3.747308232.00SALE0000000013700011.82773611670.7317070HOME0
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